Developed a Swagger Generation pipeline (10,000+ lines of production JS) that parses repositories, auto-documents endpoints with LLM-assisted annotations, and supports OpenAPI 2 to 3 conversion; eliminated manual Swagger creation for customers, significantly reducing onboarding time.

Integrated Swagger/MCP generation into both the CodeGlide.ai web platform and GitHub Actions workflow

Enhanced enterprise adoption with endpoint selection tools, schema validation, and automated error handling.

Developed an Adaptive LLM Routing System that dynamically selects between multiple foundation models (OpenAI, Anthropic) using semantic similarity, prompt classification, and historical metrics, reducing cost by 40%

Initial research into Diffusion Models for MRI generation

Developed a Diffusion Model from scratch using PyTorch, Transformers, numpy, and Convolutional Neural Networks.

Implemented key components including UNet, encoder, decoder, upsampling, downsampling, and self-attention mechanisms

Explored Audio Machine Learning for healthcare applications, focusing on signal processing and machine learning techniques

Developed an Ensemble Learning Model combining a multi-feature CNN and XGBoost for respiratory disease classification

Applied advanced audio signal processing techniques such as Mel Spectrograms, Chroma Short Time Fourier Transforms, Mel Frequency Ceptral Coefficients, Spectral Rolloff, Spectral Centroid, and Zero Crossing Rate

Implemented various data augmentation methods, including time-stretching, pitch-shifting, and noise addition

Authored a research paper available on arXiv, currently awaiting journal publication as I actively submit to additional conferences.

Designed and developed an inclusive curriculum accessible to all students by incorporating foundational AI concepts and advanced topics to cater to diverse skill levels.

Presented the curriculum and guided members through projects during meetings to achieve a hands-on learning environment and encourage active participation.

Networked with industry professionals to secure guest speakers to provide members with valuable insights and exposure to real-world AI applications.

Restructured the leadership team and club strategy to better incorporate new members and students

Developed and implemented new marketing campaigns to attract and retain members, thus significantly increasing club visibility and engagement.

Collaborated with leadership on various responsibilities, including curriculum design, marketing, financial management, project coordination, and outreach initiatives.

Helped organize AI competitions, providing members with opportunities to apply their skills in a competitive and collaborative setting.

Hosted talks for other clubs on campus to promote interdisciplinary collaboration in AI.

Networked with corporations to explore potential club sponsorships to secure resources and support to enhance club activities and member benefits.

Achieved a 500x growth in club membership under my presidency, which ultimately finished with SCAI as being one of the largest organizations on campus.

Developed and refined the curriculum to keep the material fresh for new members.

Oversaw member projects, providing technical guidance to ensure members successfully complete their projects

Facilitated effective communication between Santa Cruz Artificial Intelligence (SCAI) and the University regarding club initiatives and promotions

Developed skills in reading and analyzing research papers to inform project design and implementation strategies.

Completed various mini-projects, applying machine learning techniques to real-world problems and gaining hands-on experience in data preprocessing, model building, and result interpretation.

Collaborated with other members on group projects, most notably working on an LSTM (Long Short Term Memory) solution for identifying intron and exon sequences in DNA.